From 152c18696720a6bac8da01e148f4b005945ff293 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 23 Apr 2026 07:15:31 +0000 Subject: [PATCH 1/3] Initial plan From 6d3c63fd9bfbbe0671085ba07b63588ccc31335c Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 23 Apr 2026 07:33:55 +0000 Subject: [PATCH 2/3] feat: Build complete AI model real-time monitoring system - Backend: Node.js + Express + Socket.IO + better-sqlite3 - server.js: HTTP/WebSocket server, real-time metrics broadcast - database.js: SQLite schema, 8 model seeds, P95 latency queries - routes/api.js: REST API for models, metrics, history, alerts - services/simulator.js: Per-model realistic request simulation - services/alertManager.js: Error rate / latency / spike alerts - Frontend: Single-page Grafana-style dark dashboard - Chart.js line/doughnut/bar charts with real-time updates - 8 model cards with sparklines and health indicators - Live request log table and alert panel - Socket.IO WebSocket real-time data binding - Infrastructure: Dockerfile + docker-compose.yml - Docs: Chinese README and project-plan.md Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: LLibra1 <195303685+LLibra1@users.noreply.github.com> --- .gitignore | 5 + README.md | 166 ++- backend/.gitignore | 4 + backend/Dockerfile | 7 + backend/package-lock.json | 1905 ++++++++++++++++++++++++++ backend/package.json | 21 + backend/src/database.js | 206 +++ backend/src/routes/api.js | 143 ++ backend/src/server.js | 146 ++ backend/src/services/alertManager.js | 136 ++ backend/src/services/simulator.js | 171 +++ docker-compose.yml | 19 + docs/project-plan.md | 221 +++ frontend/index.html | 841 ++++++++++++ 14 files changed, 3990 insertions(+), 1 deletion(-) create mode 100644 .gitignore create mode 100644 backend/.gitignore create mode 100644 backend/Dockerfile create mode 100644 backend/package-lock.json create mode 100644 backend/package.json create mode 100644 backend/src/database.js create mode 100644 backend/src/routes/api.js create mode 100644 backend/src/server.js create mode 100644 backend/src/services/alertManager.js create mode 100644 backend/src/services/simulator.js create mode 100644 docker-compose.yml create mode 100644 docs/project-plan.md create mode 100644 frontend/index.html diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e4d8733 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +data/ +*.db +*.db-shm +*.db-wal +.env diff --git a/README.md b/README.md index d82f603..4cd4799 100644 --- a/README.md +++ b/README.md @@ -1 +1,165 @@ -# Watch-System \ No newline at end of file +# AI 模型监控系统 (Watch-System) + +> 实时监控开源 AI/ML 模型调用状态的可视化仪表板 + +--- + +## 📋 项目概述 + +Watch-System 是一套针对开源 AI/ML 模型（如 LLaMA2、Mistral、CodeLlama、Phi-2、Gemma、Qwen、DeepSeek 等）的**实时监控平台**。系统通过模拟真实 API 调用，持续采集请求量、响应时间、Token 用量、错误率等核心指标，并以美观的 Grafana 风格仪表板实时展示。 + +--- + +## 🏗️ 系统架构 + +``` +┌─────────────────────────────────────────────────┐ +│ 浏览器 (前端) │ +│ ┌───────────────────────────────────────────┐ │ +│ │ index.html (Chart.js + Socket.IO Client) │ │ +│ │ - 实时图表 - 模型卡片 - 告警面板 │ │ +│ └──────────────────┬────────────────────────┘ │ +└─────────────────────┼───────────────────────────┘ + │ WebSocket / HTTP +┌─────────────────────▼───────────────────────────┐ +│ Node.js 后端 (:3001) │ +│ ┌──────────┐ ┌──────────┐ ┌───────────────┐ │ +│ │ Express │ │Socket.IO │ │ REST API │ │ +│ │ Server │ │ Server │ │ /api/* │ │ +│ └────┬─────┘ └────┬─────┘ └──────┬────────┘ │ +│ │ │ │ │ +│ ┌────▼──────────────▼───────────────▼────────┐ │ +│ │ Services Layer │ │ +│ │ ┌─────────────┐ ┌──────────────────┐ │ │ +│ │ │ Simulator │ │ AlertManager │ │ │ +│ │ │ (模型模拟器) │ │ (告警管理器) │ │ │ +│ │ └──────┬──────┘ └────────┬─────────┘ │ │ +│ └─────────┼───────────────────┼─────────────┘ │ +│ │ │ │ +│ ┌─────────▼───────────────────▼─────────────┐ │ +│ │ SQLite Database (better-sqlite3) │ │ +│ │ requests 表 | models 表 │ │ +│ └────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────┘ +``` + +--- + +## ✨ 功能特性 + +| 功能 | 说明 | +|------|------| +| 🔴 实时监控 | 通过 Socket.IO WebSocket 推送，毫秒级延迟更新 | +| 📊 多维图表 | 请求量趋势折线图、请求分布饼图、响应时间柱状图 | +| 🤖 多模型支持 | 同时监控 8 个主流开源模型 | +| 🚨 智能告警 | 错误率超阈值、响应过慢、流量突刺自动告警 | +| 📝 请求日志 | 滚动显示最新 20 条请求详情 | +| 🌙 暗色主题 | Grafana/DataDog 风格深色仪表板 | +| 🐳 Docker 部署 | 一键 docker-compose up 启动 | +| 💾 持久化存储 | SQLite 本地存储历史数据 | + +--- + +## 🚀 快速启动 + +### 方式一：本地运行 + +```bash +# 1. 安装后端依赖 +cd backend +npm install + +# 2. 启动后端服务 +npm start +# 或开发模式（热重载） +npm run dev + +# 3. 打开前端 +open ../frontend/index.html +``` + +### 方式二：Docker Compose + +```bash +docker-compose up --build +# 前端访问: http://localhost:3000 +# 后端 API: http://localhost:3001 +``` + +### 环境变量 + +| 变量 | 默认值 | 说明 | +|------|--------|------| +| PORT | 3001 | 后端监听端口 | +| NODE_ENV | development | 运行环境 | +| DB_PATH | ./data/watch.db | SQLite 数据库路径 | + +--- + +## 📁 项目结构 + +``` +Watch-System/ +├── README.md +├── docker-compose.yml +├── docs/ +│ └── project-plan.md +├── frontend/ +│ └── index.html +└── backend/ + ├── Dockerfile + ├── package.json + └── src/ + ├── server.js + ├── database.js + ├── routes/ + │ └── api.js + └── services/ + ├── simulator.js + └── alertManager.js +``` + +--- + +## 📡 API 接口 + +| 方法 | 路径 | 说明 | +|------|------|------| +| GET | /api/models | 获取所有模型及实时状态 | +| GET | /api/metrics | 获取系统总体指标 | +| GET | /api/metrics/:model | 获取指定模型详细指标 | +| GET | /api/history/:model | 获取模型最近请求历史 | +| GET | /api/alerts | 获取当前活跃告警 | +| POST | /api/simulate | 手动触发一次模拟请求 | + +--- + +## 🔌 Socket.IO 事件 + +| 事件 | 方向 | 说明 | +|------|------|------| +| request_completed | 服务端→客户端 | 单次请求完成 | +| metrics_update | 服务端→客户端 | 全量指标推送（每5秒） | +| alert_created | 服务端→客户端 | 新告警产生 | +| alert_resolved | 服务端→客户端 | 告警已解除 | + +--- + +## 🤖 监控模型列表 + +| 模型 | 特征 | 响应时间 | +|------|------|----------| +| llama2-7b | 通用对话 | 800-2000ms | +| llama2-13b | 大参数通用 | 1500-4000ms | +| mistral-7b | 快速推理 | 400-1200ms | +| codellama-7b | 代码生成 | 600-1800ms | +| phi-2 | 轻量快速 | 300-900ms | +| gemma-7b | Google 开源 | 500-1500ms | +| qwen-7b | 多语言 | 400-1100ms | +| deepseek-7b | 深度推理 | 600-1600ms | + +--- + +## 📄 许可证 + +MIT License diff --git a/backend/.gitignore b/backend/.gitignore new file mode 100644 index 0000000..fc4225b --- /dev/null +++ b/backend/.gitignore @@ -0,0 +1,4 @@ +node_modules/ +data/ +*.db +.env diff --git a/backend/Dockerfile b/backend/Dockerfile new file mode 100644 index 0000000..09bf607 --- /dev/null +++ b/backend/Dockerfile @@ -0,0 +1,7 @@ +FROM node:18-alpine +WORKDIR /app +COPY package*.json ./ +RUN npm install --omit=dev +COPY . . +EXPOSE 3001 +CMD ["node", "src/server.js"] diff --git a/backend/package-lock.json b/backend/package-lock.json new file mode 100644 index 0000000..b907922 --- /dev/null +++ b/backend/package-lock.json @@ -0,0 +1,1905 @@ +{ + "name": "watch-system-backend", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": 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for Watch-System - AI Model Monitoring", + "main": "src/server.js", + "scripts": { + "start": "node src/server.js", + "dev": "nodemon src/server.js" + }, + "dependencies": { + "better-sqlite3": "^12.9.0", + "cors": "^2.8.6", + "dotenv": "^16.6.1", + "express": "^4.22.1", + "socket.io": "^4.8.3", + "uuid": "^9.0.1" + }, + "devDependencies": { + "nodemon": "^3.1.14" + } +} diff --git a/backend/src/database.js b/backend/src/database.js new file mode 100644 index 0000000..a486a19 --- /dev/null +++ b/backend/src/database.js @@ -0,0 +1,206 @@ +'use strict'; + +const Database = require('better-sqlite3'); +const path = require('path'); +const fs = require('fs'); + +const DB_PATH = process.env.DB_PATH || path.join(__dirname, '../../data/watch.db'); + +// Ensure data directory exists +fs.mkdirSync(path.dirname(DB_PATH), { recursive: true }); + +const db = new Database(DB_PATH); + +// Enable WAL mode for better concurrent read performance +db.pragma('journal_mode = WAL'); +db.pragma('synchronous = NORMAL'); + +// Create tables +db.exec(` + CREATE TABLE IF NOT EXISTS models ( + name TEXT PRIMARY KEY, + display_name TEXT NOT NULL, + description TEXT, + category TEXT + ); + + CREATE TABLE IF NOT EXISTS requests ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + model_name TEXT NOT NULL, + timestamp INTEGER NOT NULL, + response_time_ms INTEGER, + tokens_input INTEGER, + tokens_output INTEGER, + status TEXT NOT NULL DEFAULT 'success', + error_message TEXT, + FOREIGN KEY (model_name) REFERENCES models(name) + ); + + CREATE INDEX IF NOT EXISTS idx_requests_model_ts ON requests(model_name, timestamp); + CREATE INDEX IF NOT EXISTS idx_requests_ts ON requests(timestamp); +`); + +// Seed model definitions +const seedModels = db.prepare(` + INSERT OR IGNORE INTO models (name, display_name, description, category) + VALUES (?, ?, ?, ?) +`); + +const modelSeeds = [ + ['llama2-7b', 'LLaMA2 7B', 'Meta 通用对话模型，7B 参数', 'chat'], + ['llama2-13b', 'LLaMA2 13B', 'Meta 通用对话模型，13B 参数', 'chat'], + ['mistral-7b', 'Mistral 7B', 'Mistral AI 快速推理模型', 'chat'], + ['codellama-7b', 'CodeLlama 7B', 'Meta 代码生成专用模型', 'code'], + ['phi-2', 'Phi-2', 'Microsoft 轻量高效小模型', 'chat'], + ['gemma-7b', 'Gemma 7B', 'Google DeepMind 开源模型', 'chat'], + ['qwen-7b', 'Qwen 7B', '阿里云通义千问多语言模型', 'multilingual'], + ['deepseek-7b', 'DeepSeek 7B', 'DeepSeek 深度推理模型', 'chat'], +]; + +const seedTx = db.transaction(() => { + for (const seed of modelSeeds) seedModels.run(...seed); +}); +seedTx(); + +// ─── Prepared Statements ───────────────────────────────────────────────────── + +const stmtInsertRequest = db.prepare(` + INSERT INTO requests (model_name, timestamp, response_time_ms, tokens_input, tokens_output, status, error_message) + VALUES (@model_name, @timestamp, @response_time_ms, @tokens_input, @tokens_output, @status, @error_message) +`); + +const stmtGetAllModels = db.prepare(`SELECT * FROM models ORDER BY name`); + +const stmtGetModelStats = db.prepare(` + SELECT + model_name, + COUNT(*) AS total_requests, + AVG(response_time_ms) AS avg_response_time, + SUM(CASE WHEN status = 'error' THEN 1 ELSE 0 END) AS error_count, + SUM(tokens_input) AS total_tokens_in, + SUM(tokens_output) AS total_tokens_out + FROM requests + WHERE model_name = @model_name + AND timestamp >= @since +`); + +const stmtGetRecentRequests = db.prepare(` + SELECT id, model_name, timestamp, response_time_ms, tokens_input, tokens_output, status, error_message + FROM requests + WHERE model_name = @model_name + ORDER BY timestamp DESC + LIMIT @limit +`); + +const stmtGetGlobalStats = db.prepare(` + SELECT + COUNT(*) AS total_requests, + AVG(response_time_ms) AS avg_response_time, + SUM(CASE WHEN status = 'error' THEN 1 ELSE 0 END) AS error_count, + SUM(tokens_input) AS total_tokens_in, + SUM(tokens_output) AS total_tokens_out + FROM requests + WHERE timestamp >= @since +`); + +const stmtGetPercentile = db.prepare(` + SELECT response_time_ms + FROM requests + WHERE model_name = @model_name + AND timestamp >= @since + AND status = 'success' + ORDER BY response_time_ms +`); + +const stmtGetRequestsPerMinute = db.prepare(` + SELECT + (timestamp / 60000) * 60000 AS minute_bucket, + COUNT(*) AS count + FROM requests + WHERE model_name = @model_name + AND timestamp >= @since + GROUP BY minute_bucket + ORDER BY minute_bucket ASC +`); + +const stmtGetAllModelStatsRange = db.prepare(` + SELECT + model_name, + COUNT(*) AS total_requests, + AVG(response_time_ms) AS avg_response_time, + SUM(CASE WHEN status = 'error' THEN 1 ELSE 0 END) AS error_count, + SUM(tokens_input) AS total_tokens_in, + SUM(tokens_output) AS total_tokens_out + FROM requests + WHERE timestamp >= @since + GROUP BY model_name +`); + +// ─── Helper Functions ───────────────────────────────────────────────────────── + +function insertRequest(data) { + return stmtInsertRequest.run({ + model_name: data.model_name, + timestamp: data.timestamp || Date.now(), + response_time_ms: data.response_time_ms, + tokens_input: data.tokens_input, + tokens_output: data.tokens_output, + status: data.status || 'success', + error_message: data.error_message || null, + }); +} + +function getAllModels() { + return stmtGetAllModels.all(); +} + +function getModelStats(modelName, windowMs = 60 * 1000) { + const since = Date.now() - windowMs; + return stmtGetModelStats.get({ model_name: modelName, since }); +} + +function getRecentRequests(modelName, limit = 100) { + return stmtGetRecentRequests.all({ model_name: modelName, limit }); +} + +function getAggregatedMetrics(windowMs = 60 * 1000) { + const since = Date.now() - windowMs; + const global = stmtGetGlobalStats.get({ since }); + const perModel = stmtGetAllModelStatsRange.all({ since }); + return { global, perModel }; +} + +function getPerModelDetailedStats(modelName, windowMs = 60 * 1000) { + const since = Date.now() - windowMs; + const stats = stmtGetModelStats.get({ model_name: modelName, since }); + if (!stats || stats.total_requests === 0) return stats; + + // Calculate P95 latency + const rows = stmtGetPercentile.all({ model_name: modelName, since }); + let p95 = null; + if (rows.length > 0) { + const idx = Math.ceil(rows.length * 0.95) - 1; + p95 = rows[Math.min(idx, rows.length - 1)].response_time_ms; + } + + const rpmRows = stmtGetRequestsPerMinute.all({ model_name: modelName, since: Date.now() - 10 * 60 * 1000 }); + + return { + ...stats, + p95_response_time: p95, + error_rate: stats.total_requests > 0 ? (stats.error_count / stats.total_requests) * 100 : 0, + requests_per_minute: stats.total_requests / (windowMs / 60000), + tokens_per_minute: (stats.total_tokens_in + stats.total_tokens_out) / (windowMs / 60000), + rpm_history: rpmRows, + }; +} + +module.exports = { + db, + insertRequest, + getAllModels, + getModelStats, + getRecentRequests, + getAggregatedMetrics, + getPerModelDetailedStats, +}; diff --git a/backend/src/routes/api.js b/backend/src/routes/api.js new file mode 100644 index 0000000..a6f94f7 --- /dev/null +++ b/backend/src/routes/api.js @@ -0,0 +1,143 @@ +'use strict'; + +const express = require('express'); +const router = express.Router(); + +const { + getAllModels, + getModelStats, + getRecentRequests, + getAggregatedMetrics, + getPerModelDetailedStats, + insertRequest, +} = require('../database'); + +const alertManager = require('../services/alertManager'); +const simulator = require('../services/simulator'); + +// GET /api/models +router.get('/models', (req, res) => { + const models = getAllModels(); + const profiles = simulator.getModelProfiles(); + const windowMs = 60 * 1000; + + const result = models.map(m => { + const stats = getModelStats(m.name, windowMs) || {}; + const totalReq = stats.total_requests || 0; + const errCount = stats.error_count || 0; + const avgResp = stats.avg_response_time || 0; + + const profile = profiles[m.name] || {}; + let health = 'healthy'; + if (totalReq > 0) { + const errorRate = errCount / totalReq; + if (errorRate > 0.1 || avgResp > 3000) health = 'degraded'; + if (errorRate > 0.2) health = 'unhealthy'; + } + + return { + ...m, + emoji: profile.emoji || '🤖', + stats: { + total_requests: totalReq, + error_count: errCount, + avg_response_time: Math.round(avgResp), + error_rate: totalReq > 0 ? +((errCount / totalReq) * 100).toFixed(2) : 0, + requests_per_min: +(totalReq / (windowMs / 60000)).toFixed(2), + total_tokens_in: stats.total_tokens_in || 0, + total_tokens_out: stats.total_tokens_out || 0, + }, + health, + }; + }); + + res.json({ models: result, timestamp: Date.now() }); +}); + +// GET /api/metrics +router.get('/metrics', (req, res) => { + const windowMs = parseInt(req.query.window, 10) || 60 * 1000; + const { global: g, perModel } = getAggregatedMetrics(windowMs); + + const totalReq = g.total_requests || 0; + const errCount = g.error_count || 0; + + res.json({ + total_requests: totalReq, + avg_response_time: g.avg_response_time ? Math.round(g.avg_response_time) : 0, + error_rate: totalReq > 0 ? +((errCount / totalReq) * 100).toFixed(2) : 0, + total_tokens_in: g.total_tokens_in || 0, + total_tokens_out: g.total_tokens_out || 0, + active_models: perModel.filter(m => m.total_requests > 0).length, + window_ms: windowMs, + timestamp: Date.now(), + }); +}); + +// GET /api/metrics/:modelName +router.get('/metrics/:modelName', (req, res) => { + const { modelName } = req.params; + const windowMs = parseInt(req.query.window, 10) || 60 * 1000; + const stats = getPerModelDetailedStats(modelName, windowMs); + + if (!stats) { + return res.status(404).json({ error: `Model '${modelName}' not found` }); + } + + res.json({ + model_name: modelName, + total_requests: stats.total_requests || 0, + avg_response_time: stats.avg_response_time ? Math.round(stats.avg_response_time) : 0, + p95_response_time: stats.p95_response_time || 0, + error_rate: +(stats.error_rate || 0).toFixed(2), + requests_per_min: +(stats.requests_per_minute || 0).toFixed(2), + tokens_per_min: +(stats.tokens_per_minute || 0).toFixed(2), + total_tokens_in: stats.total_tokens_in || 0, + total_tokens_out: stats.total_tokens_out || 0, + rpm_history: stats.rpm_history || [], + window_ms: windowMs, + timestamp: Date.now(), + }); +}); + +// GET /api/history/:modelName +router.get('/history/:modelName', (req, res) => { + const { modelName } = req.params; + const limit = Math.min(parseInt(req.query.limit, 10) || 100, 500); + const rows = getRecentRequests(modelName, limit); + res.json({ model_name: modelName, requests: rows, count: rows.length }); +}); + +// GET /api/alerts +router.get('/alerts', (req, res) => { + res.json({ alerts: alertManager.getActiveAlerts(), timestamp: Date.now() }); +}); + +// POST /api/simulate { model?: string } +router.post('/simulate', (req, res) => { + const profiles = simulator.getModelProfiles(); + const names = Object.keys(profiles); + const modelName = req.body.model && profiles[req.body.model] + ? req.body.model + : names[Math.floor(Math.random() * names.length)]; + + const profile = profiles[modelName]; + const isError = Math.random() < profile.errorRate; + + function randInt(a, b) { return Math.floor(Math.random() * (b - a + 1)) + a; } + + const record = { + model_name: modelName, + timestamp: Date.now(), + response_time_ms: isError ? randInt(50, 300) : randInt(profile.minResponseMs, profile.maxResponseMs), + tokens_input: isError ? 0 : randInt(profile.minTokensIn, profile.maxTokensIn), + tokens_output: isError ? 0 : randInt(profile.minTokensOut, profile.maxTokensOut), + status: isError ? 'error' : 'success', + error_message: isError ? 'Manual simulation error' : null, + }; + + insertRequest(record); + res.json({ message: 'Simulation triggered', record }); +}); + +module.exports = router; diff --git a/backend/src/server.js b/backend/src/server.js new file mode 100644 index 0000000..9984a91 --- /dev/null +++ b/backend/src/server.js @@ -0,0 +1,146 @@ +'use strict'; + +require('dotenv').config(); + +const express = require('express'); +const http = require('http'); +const { Server } = require('socket.io'); +const cors = require('cors'); +const path = require('path'); + +const apiRouter = require('./routes/api'); +const simulator = require('./services/simulator'); +const alertManager = require('./services/alertManager'); +const { getAggregatedMetrics, getAllModels, getModelStats } = require('./database'); + +const PORT = process.env.PORT || 3001; +const FRONTEND_ORIGIN = process.env.FRONTEND_ORIGIN || '*'; + +// ─── App Setup ──────────────────────────────────────────────────────────────── +const app = express(); +const server = http.createServer(app); +const io = new Server(server, { + cors: { origin: FRONTEND_ORIGIN, methods: ['GET', 'POST'] }, +}); + +app.use(cors({ origin: FRONTEND_ORIGIN })); +app.use(express.json()); + +// Serve static frontend in production +if (process.env.NODE_ENV === 'production') { + app.use(express.static(path.join(__dirname, '../../frontend'))); +} + +app.use('/api', apiRouter); + +// Health check +app.get('/health', (_req, res) => res.json({ status: 'ok', uptime: process.uptime() })); + +// ─── Socket.IO ──────────────────────────────────────────────────────────────── +io.on('connection', socket => { + console.log(`[Socket.IO] Client connected: ${socket.id}`); + + // Send current state on connect + try { + const { global: g, perModel } = getAggregatedMetrics(60 * 1000); + const models = getAllModels(); + socket.emit('init', { global: g, perModel, models }); + } catch (err) { + console.error('[Socket.IO] Init error:', err.message); + } + + socket.on('disconnect', () => { + console.log(`[Socket.IO] Client disconnected: ${socket.id}`); + }); +}); + +// Forward simulator events to all connected clients +simulator.on('request_completed', payload => { + io.emit('request_completed', payload); +}); + +// Broadcast full metrics every 5 seconds +setInterval(() => { + try { + const windowMs = 60 * 1000; + const { global: g, perModel } = getAggregatedMetrics(windowMs); + + const profiles = simulator.getModelProfiles(); + const models = getAllModels(); + + const modelMetrics = models.map(m => { + const stats = perModel.find(p => p.model_name === m.name) || {}; + const profile = profiles[m.name] || {}; + const totalReq = stats.total_requests || 0; + const errCount = stats.error_count || 0; + const avgResp = stats.avg_response_time || 0; + + let health = 'healthy'; + if (totalReq > 0) { + const errorRate = errCount / totalReq; + if (errorRate > 0.1 || avgResp > 3000) health = 'degraded'; + if (errorRate > 0.2) health = 'unhealthy'; + } + + return { + name: m.name, + displayName: m.display_name, + emoji: profile.emoji || '🤖', + category: m.category, + health, + stats: { + total_requests: totalReq, + error_count: errCount, + avg_response_time: Math.round(avgResp), + error_rate: totalReq > 0 ? +((errCount / totalReq) * 100).toFixed(2) : 0, + requests_per_min: +(totalReq / (windowMs / 60000)).toFixed(2), + total_tokens_in: stats.total_tokens_in || 0, + total_tokens_out: stats.total_tokens_out || 0, + }, + }; + }); + + const totalReq = g.total_requests || 0; + const errCount = g.error_count || 0; + + io.emit('metrics_update', { + global: { + total_requests: totalReq, + avg_response_time: g.avg_response_time ? Math.round(g.avg_response_time) : 0, + error_rate: totalReq > 0 ? +((errCount / totalReq) * 100).toFixed(2) : 0, + total_tokens_in: g.total_tokens_in || 0, + total_tokens_out: g.total_tokens_out || 0, + active_models: modelMetrics.filter(m => m.stats.total_requests > 0).length, + }, + models: modelMetrics, + timestamp: Date.now(), + }); + } catch (err) { + console.error('[Metrics] Broadcast error:', err.message); + } +}, 5000); + +// Forward alert events +alertManager.on('alert_created', alert => io.emit('alert_created', alert)); +alertManager.on('alert_resolved', alert => io.emit('alert_resolved', alert)); + +// ─── Start ──────────────────────────────────────────────────────────────────── +server.listen(PORT, () => { + console.log(`[Server] Watch-System backend running on http://localhost:${PORT}`); + console.log(`[Server] Environment: ${process.env.NODE_ENV || 'development'}`); + + simulator.start(); + alertManager.start(); +}); + +// Graceful shutdown +process.on('SIGTERM', () => { + simulator.stop(); + alertManager.stop(); + server.close(() => { + console.log('[Server] Gracefully shut down'); + process.exit(0); + }); +}); + +module.exports = { app, server, io }; diff --git a/backend/src/services/alertManager.js b/backend/src/services/alertManager.js new file mode 100644 index 0000000..99cd5c9 --- /dev/null +++ b/backend/src/services/alertManager.js @@ -0,0 +1,136 @@ +'use strict'; + +const EventEmitter = require('events'); +const { getAggregatedMetrics, getModelStats } = require('../database'); + +const CHECK_INTERVAL_MS = 10000; // Check every 10 seconds +const WINDOW_MS = 60 * 1000; // 1-minute rolling window + +const THRESHOLDS = { + ERROR_RATE_PCT: 10, // > 10% errors → alert + SLOW_RESPONSE_MS: 3000, // > 3000ms avg → alert + SPIKE_MULTIPLIER: 2.5, // > 2.5× baseline requests → alert +}; + +class AlertManager extends EventEmitter { + constructor() { + super(); + this._activeAlerts = new Map(); // key: `${modelName}:${type}` + this._timer = null; + this._running = false; + this._baselineRpm = new Map(); // model → baseline requests/min + } + + _alertKey(modelName, type) { + return `${modelName}:${type}`; + } + + _createAlert(modelName, type, severity, message, value) { + const key = this._alertKey(modelName, type); + if (this._activeAlerts.has(key)) return; // deduplicate + + const alert = { + id: `${key}:${Date.now()}`, + modelName, + type, + severity, + message, + value, + createdAt: Date.now(), + }; + + this._activeAlerts.set(key, alert); + console.log(`[AlertManager] ALERT CREATED: ${message}`); + this.emit('alert_created', alert); + } + + _resolveAlert(modelName, type) { + const key = this._alertKey(modelName, type); + if (!this._activeAlerts.has(key)) return; + + const alert = this._activeAlerts.get(key); + this._activeAlerts.delete(key); + console.log(`[AlertManager] ALERT RESOLVED: ${alert.message}`); + this.emit('alert_resolved', { ...alert, resolvedAt: Date.now() }); + } + + _checkModel(modelName, stats) { + if (!stats || stats.total_requests === 0) return; + + const errorRate = (stats.error_count / stats.total_requests) * 100; + const avgResponse = stats.avg_response_time || 0; + const rpm = stats.total_requests / (WINDOW_MS / 60000); + + // ── Error rate check ────────────────────────────────────── + if (errorRate > THRESHOLDS.ERROR_RATE_PCT) { + this._createAlert( + modelName, 'high_error_rate', 'error', + `${modelName} 错误率过高: ${errorRate.toFixed(1)}%`, + errorRate + ); + } else { + this._resolveAlert(modelName, 'high_error_rate'); + } + + // ── Slow response check ─────────────────────────────────── + if (avgResponse > THRESHOLDS.SLOW_RESPONSE_MS) { + this._createAlert( + modelName, 'slow_response', 'warning', + `${modelName} 响应时间过慢: ${Math.round(avgResponse)}ms`, + avgResponse + ); + } else { + this._resolveAlert(modelName, 'slow_response'); + } + + // ── Traffic spike check ─────────────────────────────────── + const baseline = this._baselineRpm.get(modelName); + if (baseline && rpm > baseline * THRESHOLDS.SPIKE_MULTIPLIER) { + this._createAlert( + modelName, 'traffic_spike', 'warning', + `${modelName} 流量突增: ${rpm.toFixed(1)} req/min (基线: ${baseline.toFixed(1)})`, + rpm + ); + } else { + this._resolveAlert(modelName, 'traffic_spike'); + } + + // Update rolling baseline (exponential moving average) + const alpha = 0.1; + const prev = this._baselineRpm.get(modelName) || rpm; + this._baselineRpm.set(modelName, alpha * rpm + (1 - alpha) * prev); + } + + async _check() { + try { + const { perModel } = getAggregatedMetrics(WINDOW_MS); + for (const stats of perModel) { + this._checkModel(stats.model_name, stats); + } + } catch (err) { + console.error('[AlertManager] Check error:', err.message); + } + } + + start() { + if (this._running) return; + this._running = true; + this._timer = setInterval(() => this._check(), CHECK_INTERVAL_MS); + console.log('[AlertManager] Started'); + } + + stop() { + this._running = false; + if (this._timer) { + clearInterval(this._timer); + this._timer = null; + } + console.log('[AlertManager] Stopped'); + } + + getActiveAlerts() { + return Array.from(this._activeAlerts.values()); + } +} + +module.exports = new AlertManager(); diff --git a/backend/src/services/simulator.js b/backend/src/services/simulator.js new file mode 100644 index 0000000..1164a99 --- /dev/null +++ b/backend/src/services/simulator.js @@ -0,0 +1,171 @@ +'use strict'; + +const EventEmitter = require('events'); +const { insertRequest } = require('../database'); + +// Model personality profiles +const MODEL_PROFILES = { + 'llama2-7b': { + displayName: 'LLaMA2 7B', + minResponseMs: 800, maxResponseMs: 2000, + minTokensIn: 50, maxTokensIn: 512, + minTokensOut: 100, maxTokensOut: 800, + errorRate: 0.03, + emoji: '🦙', + }, + 'llama2-13b': { + displayName: 'LLaMA2 13B', + minResponseMs: 1500, maxResponseMs: 4000, + minTokensIn: 80, maxTokensIn: 600, + minTokensOut: 150, maxTokensOut: 1200, + errorRate: 0.04, + emoji: '🦙', + }, + 'mistral-7b': { + displayName: 'Mistral 7B', + minResponseMs: 400, maxResponseMs: 1200, + minTokensIn: 60, maxTokensIn: 450, + minTokensOut: 80, maxTokensOut: 700, + errorRate: 0.02, + emoji: '🌪️', + }, + 'codellama-7b': { + displayName: 'CodeLlama 7B', + minResponseMs: 600, maxResponseMs: 1800, + minTokensIn: 100, maxTokensIn: 800, + minTokensOut: 200, maxTokensOut: 1500, + errorRate: 0.05, + emoji: '💻', + }, + 'phi-2': { + displayName: 'Phi-2', + minResponseMs: 300, maxResponseMs: 900, + minTokensIn: 30, maxTokensIn: 300, + minTokensOut: 50, maxTokensOut: 500, + errorRate: 0.02, + emoji: '⚡', + }, + 'gemma-7b': { + displayName: 'Gemma 7B', + minResponseMs: 500, maxResponseMs: 1500, + minTokensIn: 60, maxTokensIn: 480, + minTokensOut: 90, maxTokensOut: 750, + errorRate: 0.03, + emoji: '💎', + }, + 'qwen-7b': { + displayName: 'Qwen 7B', + minResponseMs: 400, maxResponseMs: 1100, + minTokensIn: 70, maxTokensIn: 500, + minTokensOut: 100, maxTokensOut: 800, + errorRate: 0.03, + emoji: '🌏', + }, + 'deepseek-7b': { + displayName: 'DeepSeek 7B', + minResponseMs: 600, maxResponseMs: 1600, + minTokensIn: 80, maxTokensIn: 550, + minTokensOut: 120, maxTokensOut: 900, + errorRate: 0.04, + emoji: '🔍', + }, +}; + +const MODEL_NAMES = Object.keys(MODEL_PROFILES); + +function randInt(min, max) { + return Math.floor(Math.random() * (max - min + 1)) + min; +} + +function randFloat(min, max) { + return Math.random() * (max - min) + min; +} + +class Simulator extends EventEmitter { + constructor() { + super(); + this._timer = null; + this._running = false; + } + + _simulateOne() { + const modelName = MODEL_NAMES[Math.floor(Math.random() * MODEL_NAMES.length)]; + const profile = MODEL_PROFILES[modelName]; + + const isError = Math.random() < profile.errorRate; + const responseTime = isError + ? randInt(50, 300) + : randInt(profile.minResponseMs, profile.maxResponseMs); + + const tokensIn = isError ? 0 : randInt(profile.minTokensIn, profile.maxTokensIn); + const tokensOut = isError ? 0 : randInt(profile.minTokensOut, profile.maxTokensOut); + + const errorMessages = [ + 'Rate limit exceeded', + 'Context length exceeded', + 'Out of memory', + 'Model loading timeout', + 'Invalid request format', + ]; + + const record = { + model_name: modelName, + timestamp: Date.now(), + response_time_ms: responseTime, + tokens_input: tokensIn, + tokens_output: tokensOut, + status: isError ? 'error' : 'success', + error_message: isError ? errorMessages[Math.floor(Math.random() * errorMessages.length)] : null, + }; + + try { + insertRequest(record); + } catch (err) { + // Non-fatal: log and continue + console.error('[Simulator] DB insert error:', err.message); + } + + this.emit('request_completed', { + model: modelName, + displayName: profile.displayName, + emoji: profile.emoji, + responseTime, + tokensIn, + tokensOut, + status: record.status, + errorMessage: record.error_message, + timestamp: record.timestamp, + }); + } + + _scheduleNext() { + if (!this._running) return; + const delay = randInt(200, 800); + this._timer = setTimeout(() => { + this._simulateOne(); + this._scheduleNext(); + }, delay); + } + + start() { + if (this._running) return; + this._running = true; + console.log('[Simulator] Started'); + this._scheduleNext(); + } + + stop() { + this._running = false; + if (this._timer) { + clearTimeout(this._timer); + this._timer = null; + } + console.log('[Simulator] Stopped'); + } + + getModelProfiles() { + return MODEL_PROFILES; + } +} + +module.exports = new Simulator(); diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..bf56bec --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,19 @@ +version: '3.8' +services: + backend: + build: ./backend + ports: + - "3001:3001" + environment: + - NODE_ENV=production + - PORT=3001 + volumes: + - ./data:/app/data + frontend: + image: nginx:alpine + ports: + - "3000:80" + volumes: + - ./frontend:/usr/share/nginx/html + depends_on: + - backend diff --git a/docs/project-plan.md b/docs/project-plan.md new file mode 100644 index 0000000..274428c --- /dev/null +++ b/docs/project-plan.md @@ -0,0 +1,221 @@ +# Watch-System 项目规划文档 + +## 一、项目背景 + +随着开源大语言模型（LLM）的爆发式增长，企业和开发者在生产环境中部署了越来越多的 AI 模型服务。如何实时掌握这些模型的运行状态、性能指标以及异常情况，成为 MLOps 工程师面临的核心挑战之一。 + +Watch-System 旨在提供一套轻量、可扩展的监控解决方案，针对开源模型（LLaMA2、Mistral、CodeLlama 等）的 API 调用进行全方位监控，帮助团队： + +- 快速发现性能瓶颈（响应时间过长、错误率上升） +- 掌握各模型的资源消耗（Token 使用量） +- 在异常发生时及时告警，缩短 MTTR（平均故障恢复时间） + +--- + +## 二、系统架构 + +### 2.1 整体架构 + +系统采用前后端分离架构，后端为 Node.js 服务，前端为单页应用（SPA）。 + +``` +前端 (SPA) 后端 (Node.js) 数据层 + │ │ │ + │── HTTP GET ──────► REST API │ + │ │── 查询 ──────────► SQLite DB + │◄─ WebSocket ────── Socket.IO │ + │ (实时推送) │── 写入 ──────────► SQLite DB + │ + 模拟器 / 告警管理器 +``` + +### 2.2 技术选型理由 + +| 技术 | 版本 | 选型理由 | +|------|------|----------| +| Node.js + Express | 18.x / 4.x | 轻量、生态丰富、异步友好 | +| Socket.IO | 4.x | 自动降级、断线重连、房间广播 | +| better-sqlite3 | 9.x | 同步 API、高性能、零配置 | +| Chart.js | 4.x | 轻量图表库，支持实时更新 | +| Docker Compose | 3.8 | 一键编排，环境一致性 | + +--- + +## 三、模块拆解 + +### 3.1 后端模块 + +#### server.js（主入口） +- 初始化 Express 应用 +- 挂载 CORS、JSON 中间件 +- 注册 `/api` 路由 +- 启动 Socket.IO 服务 +- 启动模拟器和告警管理器 +- 监听指定端口 + +#### database.js（数据层） +- 初始化 SQLite 数据库 +- 创建 `requests` 表和 `models` 表 +- 提供以下 Helper 函数： + - `insertRequest()` - 写入请求记录 + - `getModelStats()` - 查询模型统计数据 + - `getRecentRequests()` - 查询近期请求列表 + - `getAggregatedMetrics()` - 聚合全局指标 + +#### routes/api.js（REST API） +- `GET /api/models` - 返回所有模型列表及实时状态 +- `GET /api/metrics` - 全局聚合指标 +- `GET /api/metrics/:modelName` - 单模型指标（含 P95 延迟） +- `GET /api/history/:modelName` - 最近 100 条请求历史 +- `GET /api/alerts` - 活跃告警列表 +- `POST /api/simulate` - 手动触发单次模拟 + +#### services/simulator.js（模拟器） +- 定义 8 个模型的特征参数（响应时间范围、Token 范围、错误率） +- 随机化请求间隔（200-800ms） +- 每次模拟完成后： + 1. 写入数据库 + 2. 触发 `request_completed` 事件 +- 支持启动/停止控制 + +#### services/alertManager.js（告警管理） +- 定期检查指标阈值： + - 错误率 > 10%：ERROR 级别告警 + - 平均响应时间 > 3000ms：WARNING 级别告警 + - 请求量突增 > 200%：WARNING 级别告警 +- 告警去重（同一模型同一类型不重复创建） +- 指标恢复后自动解除告警 +- 触发 `alert_created` / `alert_resolved` 事件 + +### 3.2 前端模块 + +#### 布局结构 +``` +┌──────────────── Header ───────────────────┐ +│ AI 模型监控系统 [实时时钟] [状态] │ +├──────── Stats Cards (4列) ────────────────┤ +│ 总请求数 │ 活跃模型 │ 均响应时间 │ 错误率 │ +├────── Charts (左2/3 + 右1/3) ─────────────┤ +│ 请求量趋势折线图 │ 请求分布饼图 │ +├────── 响应时间柱状图 ──────────────────────┤ +│ 各模型平均响应时间横向柱状图 │ +├──── Model Cards Grid (4x2) ───────────────┤ +│ [模型卡片×8] │ +├──── Live Log + Alerts ────────────────────┤ +│ 实时请求日志表格 │ 告警面板 │ +└───────────────────────────────────────────┘ +``` + +--- + +## 四、数据流设计 + +### 4.1 实时数据流 + +``` +Simulator.js + │ + │ 每 200-800ms 生成一条模拟请求 + ▼ +database.insertRequest() + │ + │ 写入 SQLite + ▼ +EventEmitter.emit('request_completed', payload) + │ + ▼ +server.js 监听事件 + │ + │ io.emit('request_completed', payload) + ▼ +所有连接的前端客户端 + │ + ▼ +更新实时日志 + 更新模型卡片计数器 +``` + +### 4.2 定时指标推送流 + +``` +server.js setInterval(5000) + │ + │ 调用 database.getAggregatedMetrics() + ▼ +io.emit('metrics_update', allMetrics) + │ + ▼ +前端更新： + - Stats Cards 数字动画 + - 折线图新增数据点 + - 饼图重绘 + - 柱状图重绘 + - 模型卡片状态更新 +``` + +### 4.3 告警流 + +``` +alertManager.js setInterval(10000) + │ + │ 查询最近 1 分钟各模型指标 + ▼ +检查阈值条件 + │ + ├── 新告警 → emit('alert_created') + │ → 前端显示告警横幅 + │ + └── 告警恢复 → emit('alert_resolved') + → 前端标记告警已解除 +``` + +--- + +## 五、数据库设计 + +### requests 表 + +| 字段 | 类型 | 说明 | +|------|------|------| +| id | INTEGER PRIMARY KEY | 自增主键 | +| model_name | TEXT NOT NULL | 模型标识符 | +| timestamp | INTEGER NOT NULL | Unix 时间戳（毫秒） | +| response_time_ms | INTEGER | 响应时间（毫秒） | +| tokens_input | INTEGER | 输入 Token 数 | +| tokens_output | INTEGER | 输出 Token 数 | +| status | TEXT | success / error | +| error_message | TEXT | 错误信息（可空） | + +### models 表 + +| 字段 | 类型 | 说明 | +|------|------|------| +| name | TEXT PRIMARY KEY | 模型唯一标识 | +| display_name | TEXT | 展示名称 | +| description | TEXT | 模型描述 | +| category | TEXT | 分类（chat/code/multilingual） | + +--- + +## 六、开发里程碑 + +| 阶段 | 任务 | 状态 | +|------|------|------| +| M1 | 项目初始化、目录结构、package.json | ✅ 完成 | +| M2 | 数据库设计与实现（database.js） | ✅ 完成 | +| M3 | 模型模拟器实现（simulator.js） | ✅ 完成 | +| M4 | REST API 路由实现（api.js） | ✅ 完成 | +| M5 | 告警管理器（alertManager.js） | ✅ 完成 | +| M6 | 主服务入口（server.js） | ✅ 完成 | +| M7 | 前端仪表板（index.html） | ✅ 完成 | +| M8 | Docker 化部署配置 | ✅ 完成 | +| M9 | 端到端联调测试 | ✅ 完成 | + +--- + +## 七、扩展规划 + +- **多实例支持**：引入 Redis Pub/Sub 替换内存事件总线，支持水平扩展 +- **真实接入**：对接 Ollama / vLLM 等本地推理框架的真实 API +- **用户认证**：添加 JWT 鉴权，支持多租户监控 +- **数据导出**：支持 Prometheus 指标格式导出，对接 Grafana +- **移动端适配**：PWA 支持，手机端查看告警推送 diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..0f89314 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,841 @@ + + + + + + AI 模型监控系统 + + + + + + + +
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AI 模型监控系统

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Watch-System · Real-time Model Observability
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+ + + + From ef545385dd62d99315748156fa1137c79ee4c8c4 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 23 Apr 2026 07:49:57 +0000 Subject: [PATCH 3/3] Fix CORS security, local JS libs, and code review improvements Agent-Logs-Url: https://github.com/LLibra1/Watch-System/sessions/28e34e43-2d8e-4fc8-961b-126c83bb63d2 Co-authored-by: LLibra1 <195303685+LLibra1@users.noreply.github.com> --- .gitignore | 3 +++ README.md | 21 +++++++++++++-------- backend/src/server.js | 21 +++++++++++++++------ backend/src/services/simulator.js | 7 ++++++- frontend/index.html | 11 ++++++++--- frontend/lib/chart.umd.min.js | 14 ++++++++++++++ frontend/lib/socket.io.min.js | 7 +++++++ 7 files changed, 66 insertions(+), 18 deletions(-) create mode 100644 frontend/lib/chart.umd.min.js create mode 100644 frontend/lib/socket.io.min.js diff --git a/.gitignore b/.gitignore index e4d8733..0d7f3f2 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,6 @@ data/ *.db-shm *.db-wal .env +node_modules/ +npm-debug.log* +.DS_Store diff --git a/README.md b/README.md index 4cd4799..23de5a4 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,8 @@ > 实时监控开源 AI/ML 模型调用状态的可视化仪表板 +![AI 模型监控系统仪表板](https://github.com/user-attachments/assets/1ba8e290-5d92-4eeb-b949-62a6374e10ce) + --- ## 📋 项目概述 @@ -69,13 +71,12 @@ Watch-System 是一套针对开源 AI/ML 模型（如 LLaMA2、Mistral、CodeLla cd backend npm install -# 2. 启动后端服务 -npm start -# 或开发模式（热重载） -npm run dev +# 2. 生产模式启动（同时提供前端服务） +NODE_ENV=production npm start +# 浏览器访问: http://localhost:3001 -# 3. 打开前端 -open ../frontend/index.html +# 或开发模式（仅 API，前端直接打开 frontend/index.html） +npm run dev ``` ### 方式二：Docker Compose @@ -91,8 +92,9 @@ docker-compose up --build | 变量 | 默认值 | 说明 | |------|--------|------| | PORT | 3001 | 后端监听端口 | -| NODE_ENV | development | 运行环境 | +| NODE_ENV | development | 运行环境（production 时同时提供前端静态文件） | | DB_PATH | ./data/watch.db | SQLite 数据库路径 | +| FRONTEND_ORIGIN | localhost:3000,3001 | 开发模式允许的 CORS 来源（逗号分隔） | --- @@ -105,7 +107,10 @@ Watch-System/ ├── docs/ │ └── project-plan.md ├── frontend/ -│ └── index.html +│ ├── index.html +│ └── lib/ # 本地 JS 库（无需 CDN） +│ ├── chart.umd.min.js +│ └── socket.io.min.js └── backend/ ├── Dockerfile ├── package.json diff --git a/backend/src/server.js b/backend/src/server.js index 9984a91..e5cdb0f 100644 --- a/backend/src/server.js +++ b/backend/src/server.js @@ -14,20 +14,29 @@ const alertManager = require('./services/alertManager'); const { getAggregatedMetrics, getAllModels, getModelStats } = require('./database'); const PORT = process.env.PORT || 3001; -const FRONTEND_ORIGIN = process.env.FRONTEND_ORIGIN || '*'; +const isProduction = process.env.NODE_ENV === 'production'; + +// In production the frontend is served from the same origin, so CORS is not needed. +// In development, restrict to localhost only (no wildcard). +const DEV_ORIGINS = process.env.FRONTEND_ORIGIN + ? process.env.FRONTEND_ORIGIN.split(',').map(s => s.trim()) + : [`http://localhost:3000`, `http://localhost:${PORT}`, `http://127.0.0.1:${PORT}`]; + +const corsOptions = { + origin: isProduction ? false : DEV_ORIGINS, + methods: ['GET', 'POST'], +}; // ─── App Setup ──────────────────────────────────────────────────────────────── const app = express(); const server = http.createServer(app); -const io = new Server(server, { - cors: { origin: FRONTEND_ORIGIN, methods: ['GET', 'POST'] }, -}); +const io = new Server(server, { cors: corsOptions }); -app.use(cors({ origin: FRONTEND_ORIGIN })); +app.use(cors(corsOptions)); app.use(express.json()); // Serve static frontend in production -if (process.env.NODE_ENV === 'production') { +if (isProduction) { app.use(express.static(path.join(__dirname, '../../frontend'))); } diff --git a/backend/src/services/simulator.js b/backend/src/services/simulator.js index 1164a99..eb7282d 100644 --- a/backend/src/services/simulator.js +++ b/backend/src/services/simulator.js @@ -3,6 +3,11 @@ const EventEmitter = require('events'); const { insertRequest } = require('../database'); +// Interval range (ms) between simulated requests. +// Keeps the simulator from running too fast or too slow for demo purposes. +const MIN_REQUEST_DELAY_MS = 200; +const MAX_REQUEST_DELAY_MS = 800; + // Model personality profiles const MODEL_PROFILES = { 'llama2-7b': { @@ -140,7 +145,7 @@ class Simulator extends EventEmitter { _scheduleNext() { if (!this._running) return; - const delay = randInt(200, 800); + const delay = randInt(MIN_REQUEST_DELAY_MS, MAX_REQUEST_DELAY_MS); this._timer = setTimeout(() => { this._simulateOne(); this._scheduleNext(); diff --git a/frontend/index.html b/frontend/index.html index 0f89314..da5d2df 100644 --- a/frontend/index.html +++ b/frontend/index.html @@ -4,8 +4,8 @@ AI 模型监控系统 - - + +